A fast GA-ANN model and application in multi-objective optimization of the sealing ring for the subsea pipeline connector with regard of the penetration load

被引:3
|
作者
Jiao, Kefeng [1 ]
Yun, Feihong [1 ,2 ]
Hao, Xiaoquan [1 ]
Wang, Gang [1 ]
Yao, Shaoming [1 ]
Jia, Peng [1 ]
Wang, Xiangyu [1 ]
Wang, Liquan [1 ]
机构
[1] Harbin Engn Univ, Coll Mech & Elect Engn, Habin 15000, Peoples R China
[2] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Habin 15000, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Artificial neural network; Genetic algorithm; NSGA-II; RSM; Sealing performance; Structure optimization; GENETIC ALGORITHM; RESPONSE-SURFACE; PERFORMANCE; PREDICTION; DESIGN;
D O I
10.1007/s12206-023-1225-8
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In this paper, a finite element method is proposed to predict the contact pressure of the metal seal in the subsea pipeline connector with regard of the penetration load on the critical sealing surface. Further, a fast GA-ANN model is proposed on the basis of GA-ANN to optimize the sealing structure of the connector, which uses only 3-6 % of the calculation time compared to the traditional GA-ANN model. The fast GA-ANN is further coupled with NSGA-II in the multiple objective optimization of the sealing structure and the results are compared with that of the response surface methodology (RSM). In terms of the number of valid candidate points and the optimal candidate point, NSGA-II coupled fast GA-ANN model performs much better than RSM. The hydrostatic pressure tests were carried out with the optimal sealing structure by the fast GA-ANN and the results meet the design requirements very well.
引用
收藏
页码:309 / 322
页数:14
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